Hybrid++: The Bridge between PDE Models and Deep Learning for Gamma Noise Removal
Multiplicative gamma noise is one of the dominant noise factors in Synthetic Aperture Radar (SAR) and medical ultrasound images. They are dependent on pixel level noises due to which they are highly varying across the image and harder to handle as compared to additive noise. The denoising methods to address this noise currently include classical partial differential equation (PDE) methods which are good for interpretation but lack the restoration ability as compared to the state-of-the-art models while deep convolutional networks like DnCNN achieve high performance but at the cost of transparency due to which practitioners are skeptical to use them in high-risk critical fields like medici...
| Authors | Mahipal Jetta, Sujato Dutta |
|---|---|
| Primary category | eess.IV |
| Published | 6 October 2026 |
| Last updated at source | 6 October 2026 |
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